code: def __init__(
      self,
      network,
      memory_size,
      use_double_dqn=False,
      target_network=None,
      n_replace_target=500,
      dueling=True,
      prioritized=True,
  ):
      self.network = network
      self.trainer = dy.AdamTrainer(network.pc)
      self.trainer.set_clip_threshold(1.0)
      self.trainer.set_learning_rate(5e-4)
      self.epsilon = 1.0
      self.epsilon_decrease = 1e-4
      self.epsilon_lower = 0.05
      self.reward_decay = 0.99
      self.learn_step = 0
  
      self.use_double_dqn = use_double_dqn
      if use_double_dqn:
          self.target_network = target_network
          self.n_replace_target = n_replace_target
          self.target_network.update(network)
      self.dueling = dueling
      self.prioritized = prioritized
      if prioritized:
          self.beta = 0.0
          self.beta_increase = self.epsilon_decrease
  
      self.memory = (
          PrioritizedMemory(memory_size)
          if prioritized
          else Memory(memory_size)
      )
  
file_path: dynet/examples/reinforcement-learning/dqn.py
imports: import dynet
  import numpy
  from memory import Memory, PrioritizedMemory
label_desc: Use image node_id 1 to create a new DeepQNetwork object from inherited base classes: object with example: obj = DeepQNetwork(network, memory_size, use_double_dqn, target_network, n_replace_target, dueling, prioritized)